The Fault Diagnosis Research on Nonlinear Feature Extraction with Kernel Technology

The dimensionality disaster problem exist in pattern recognition process, the fault diagnosis method on nonlinear feature kernel extraction is presented here. The fisher linear discriminant analysis method is extended to nonlinear fields by kernel technology, the original feature space is mapped into observation space for features linearization, The fault pattern is classified by fisher linear discriminant analysis in minimum criteria, The problem on nonlinear feature separability and small sample size are solved. an fault diagnosis illustration verifies this method.